World’s largest sports and humanitarian event builds legacy of inclusion with data-driven technology

Special Olympics World Games Abu Dhabi uses SAS^® Analytics and AI solutions to keep athletes safe and fans engaged

In 2019, thousands of athletes from around the world descended on the United Arab Emirates to compete in the year’s largest humanitarian sports event. And while winning a gold medal was the objective for some, simply participating in the event was the ultimate prize for most.

Welcome to the Special Olympics World Games. Like the Olympics, the Special Olympic summer games occur every four years and include events like basketball, soccer and track and field. But unlike the Olympics – which centers on the podium – the Special Olympics is a celebration of inclusion. For the Special Olympics World Games Abu Dhabi 2019, 7,500 athletes from 190 countries came together for the chance to meet new friends and see new cultures.

For these athletes, experience trumps all. For the organizing committee, on the other hand, the drive to create an extraordinary experience only raises the stakes for a group already challenged to transport and accommodate thousands of participants across multiple cities. In addition, disabled athletes have greater medical needs, requiring organizers to closely monitor health issues and get help to those who need it quickly.

The Special Olympics partnered with SAS to address these challenges with an artificial intelligence (AI), advanced analytics and data management platform. According to Peter Wheeler, Chief Executive Officer of the Special Olympics World Games Abu Dhabi 2019, this is the first time in the organization’s 50-year history where technology has played such a vital role.

“The World Games in Abu Dhabi marks the start of the next 50 years of Special Olympics, so we wanted to ensure these games were innovative and technologically advanced,” Wheeler says. “Using the latest data-gathering methods to monitor our athletes and other constituents enables us not only to deliver excellence at the World Games, but it creates a legacy of valuable data that will help us redefine the future of the Special Olympics movement.”

Predicting medical issues with IoT and AI

The first step in any analytics project is getting your hands on the data. With only three months before the opening ceremony, organizers worked with SAS to quickly form a team of volunteer data scientists spread across five continents. The team worked around the clock to combine 25 data sets from venues, ticketing, registration, medical information, volunteers, travel, accommodation, catering, scheduling and others. Data was uploaded to the cloud for analytics.

The first analytics use case was priority No. 1 – keeping athletes safe and healthy. Because every Special Olympics athlete has particular health conditions and requirements, it’s vital for organizers to track key medical information and use real-time data to ensure athlete safety. To facilitate this critical work, each athlete was given a smart watch that streamed IoT-level health and location data to a central dashboard where medical professionals could observe everyone.

Taking this a step further, organizers applied SAS AI solutions to streamline health data to predict when health issues might occur. This allowed them to strategically position health personnel, equipment and vehicles near certain athletes to expedite care should a problem arise.

In total, 1,529 medical incidents occurred during the weeklong event, some of which were serious incidents like seizures and asthma attacks. In some cases, quick response times aided by analytics made the difference between life and death, according to Yousef Alhammadi, Chief Intelligence Officer of Special Olympics World Games Abu Dhabi 2019.

“Lives were saved using analytics during the Special Olympics,” Alhammadi says.

Ensuring a great fan experience

In addition to ensuring athlete safety, organizers were focused on making the event a pleasurable experience for spectators. This included 500,000 family members, delegations and fans in attendance.

With real-time data streaming from ticketing, travel, accommodation and guest management systems, organizers used machine learning algorithms to identify patterns and predict fan interests. As a result, fans received personalized messages with event recommendations, ticket availability and travel times, among other tips.

“We used SAS to maximize the fan experience,” Alhammadi says.

Social media was another rich data source to improve the fan experience – for both the spectators attending the games as well as millions of viewers around the globe who were tuning in to the broadcast coverage. Text analytics software was used to comb through 68 billion social impressions, revealing what fans liked and disliked about the games. This ability to automatically analyze fan sentiment from social media allowed organizers to improve communications during the event and make data-driven policies after the event.